Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

Koubâa, Anis; Ammar, Adel; Benjdira, Bilel; Al-Hadid, Abdullatif; Kawaf, Belal; Al-Yahri, Saleh Ali; Babiker, Abdelrahman; Assaf, Koutaiba; Ras, Mohannad Ba

doi:10.1109/cdma47397.2020.00024

Cited by 24 publications

(15 citation statements)

References 18 publications

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“…This recognition is undertaken through classification among various viruses. We profited from the progress conducted on deep learning [1][2][3][4][5][6][7]. Deep Learning is a sub-field of machine learning dealing with algorithms in tune with the structure and function of the brain-known as artificial neural networks.…”

Section: Covid-19 Diagnosis In Chest X-rays Imagesmentioning

confidence: 99%

COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

et al. 2021

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The COVID-19 disease has spread all over the world, representing an intriguing challenge for humanity as a whole. The efficient diagnosis of humans infected by COVID-19 still remains an increasing need worldwide. The chest X-ray imagery represents, among others, one attractive means to detect COVID-19 cases efficiently. Many studies have reported the efficiency of using deep learning classifiers in diagnosing COVID-19 from chest X-ray images. They conducted several comparisons among a subset of classifiers to identify the most accurate. In this paper, we investigate the potential of the combination of state-of-the-art classifiers in achieving the highest possible accuracy for the detection of COVID-19 from X-ray. For this purpose, we conducted a comprehensive comparison study among 16 state-of-the-art classifiers. To the best of our knowledge, this is the first study considering this number of classifiers. This paper’s innovation lies in the methodology that we followed to develop the inference system that allows us to detect COVID-19 with high accuracy. The methodology consists of three steps: (1) comprehensive comparative study between 16 state-of-the-art classifiers; (2) comparison between different ensemble classification techniques, including hard/soft majority, weighted voting, Support Vector Machine, and Random Forest; and (3) finding the combination of deep learning models and ensemble classification techniques that lead to the highest classification confidence on three classes. We found that using the Majority Voting approach is an adequate strategy to adopt in general cases for this task and may achieve an average accuracy up to 99.314%.

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Section: Covid-19 Diagnosis In Chest X-rays Imagesmentioning

confidence: 99%

COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

et al. 2021

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“…The posture is about how the people hold their body and position the limbs. Within the advancement of the technology, the human has chosen the sedentary lifestyle which leading to less physical activity and movement [14,15,16,17,18,19]. The long time sitting during the work or study leads to decrease in muscle strength.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks

et al. 2021

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The posture detection received lots of attention in the fields of human sensing and artificial intelligence. Posture detection can be used for the monitoring health status of elderly remotely by identifying their postures such as standing, sitting and walking. Most of the current studies used traditional machine learning classifiers to identify the posture. However, these methods do not perform well to detect the postures accurately. Therefore, in this study, we proposed a novel hybrid approach based on machine learning classifiers (i. e., support vector machine (SVM), logistic regression (KNN), decision tree, Naive Bayes, random forest, Linear discrete analysis and Quadratic discrete analysis) and deep learning classifiers (i. e., 1D-convolutional neural network (1D-CNN), 2D-convolutional neural network (2D-CNN), LSTM and bidirectional LSTM) to identify posture detection. The proposed hybrid approach uses prediction of machine learning (ML) and deep learning (DL) to improve the performance of ML and DL algorithms. The experimental results on widely benchmark dataset are shown and results achieved an accuracy of more than 98%.

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Section: Introductionmentioning

confidence: 99%

“…7,9,11,17 Many pilgrims also maximize prayers (Salat) during Hajj, 18 which involve repetitive motions between a series of postures, including standing, bowing, prostration, and sitting. 19 In addition, individuals' normal daily routines may be changed during the Hajj period as pilgrims move between places where the geography and climate are different, stay in tents and may neglect their self-health management while they are preoccupied with religious rituals. 7,12 Many studies have been conducted to investigate health issues in pilgrims, with the main focus on infectious diseases [9][10][11][12]14,20,21 and other diseases, including cardiovascular diseases, neurological disorders, trauma, gastrointestinal problems, diabetes, heat exhaustion and dermatological diseases.…”

Section: Introductionmentioning

confidence: 99%

The Prevalence and Factors Associated with Musculoskeletal Pain Among Pilgrims During the Hajj

Alshehri¹,

Alzaidi²,

Alasmari³

et al. 2021

JPR

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Background Musculoskeletal pain is a primary burden on individuals as well as social and health care systems. Annually, 2–3 million pilgrims perform the Hajj in Mecca, Saudi Arabia. The Hajj is highly physically demanding because pilgrims generally move by foot for long distances among a series of religious sites, an effort that may exceed their typical levels of physical activity. To understand the impact of musculoskeletal pain on the completion of the Hajj, it is first necessary to evaluate the extent of the problem. Accordingly, this study aimed to estimate the prevalence of musculoskeletal pain and associated factors among pilgrims during the Hajj. Methods A cross-sectional survey was conducted during the period of the Hajj. The participants were adult pilgrims ≥ 18 years of age. Data regarding demographics, the prevalence of falls and the point prevalence of musculoskeletal pain by anatomical site were recorded. Participants were allowed to report more than one site of pain. Prevalence, crude and adjusted risk ratios were calculated. Results A total of 1715 pilgrims were included in the analysis. The prevalence of falls was 13.76%. The prevalence of overall musculoskeletal pain (pain at any site) was 80.46%. Musculoskeletal pain was most commonly reported in the ankle/foot (38.34%), leg (29.89%), lower back (28.47%) and knee (21.84%). In general, musculoskeletal pain at multiple sites was more common in females and in older and obese individuals. However, there were variations in the importance of sex, age and body mass index as associated factors across different pain sites. Conclusion Musculoskeletal pain is common among pilgrims. Unlike most populations examined in other studies, ankle/foot pain was the most common in pilgrims. These data provide guidance for potential preventative programs and the allocation of resources to optimize pilgrims’ experiences and ability to complete the Hajj.

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Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

Cited by 24 publications

References 18 publications

COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks

The Prevalence and Factors Associated with Musculoskeletal Pain Among Pilgrims During the Hajj

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